Technology Hype Can Make Generative AI Progress Harder to Evaluate Clearly Impact

Technology Hype Can Make Generative AI Progress Harder to Evaluate Clearly

Learn how generative AI hype distorts progress claims—and how to evaluate models using reliability, benchmarks vs. real work, edge cases, and true costs.

Korin Kashtan
High User Expectations Can Expose the Limits of Generative AI Products Impact

High User Expectations Can Expose the Limits of Generative AI Products

High user expectations expose generative AI reliability limits—how inconsistency, context gaps, and tone errors break workflows and erode trust.

Pamela Andrew
Competition Between AI Platforms Can Accelerate New Model Development Impact

Competition Between AI Platforms Can Accelerate New Model Development

How AI platform rivalry accelerates new model development through tooling, telemetry, infrastructure, and ecosystem pull—while increasing lock-in, safety, and fragmentation risks.

Madison Evans
Real-World Adoption Can Matter More Than AI Hype Impact

Real-World Adoption Can Matter More Than AI Hype

Real-world AI adoption beats hype: how to choose workflows, measure ROI, uncover hidden costs, and ship AI that sticks with real usage metrics.

Vicky Louisa
The ChatGPT Effect Is Spreading Across More Digital Tools Impact

The ChatGPT Effect Is Spreading Across More Digital Tools

Explore the “ChatGPT effect” as chat assistants spread through software—and learn when they speed work, where they break, and how to choose safer AI tools.

Susan Kelly
AI Content Is Changing How Online Publishing Is Organized Impact

AI Content Is Changing How Online Publishing Is Organized

AI in online publishing is reshaping org charts, workflows, and governance—shifting value from drafting to QA, sourcing, distribution, and standards.

Elva Flynn
Real-World Use Can Quickly Change Expectations Around New Models Impact

Real-World Use Can Quickly Change Expectations Around New Models

Learn why new AI model demos break down in production and how to reset expectations with real-world testing, measurement, and rollout trade-offs.

Sean William
AI Literacy Matters More Than Knowing Every New AI Tool Impact

AI Literacy Matters More Than Knowing Every New AI Tool

AI literacy beats chasing every new AI tool: learn prompts, evaluation, and judgment, plus privacy/IP limits, to use AI reliably at work.

Christin Shatzman
AI Copyright Questions Extend From Training Data to Generated Content Impact

AI Copyright Questions Extend From Training Data to Generated Content

Explore AI copyright questions from training data to AI-generated content: what counts as copying, output similarity, ownership, and practical risk checks.

Tessa Rodriguez
Emotional Attachment Is Becoming a New Issue for AI Companions Impact

Emotional Attachment Is Becoming a New Issue for AI Companions

Emotional attachment to AI companions is rising. Learn why it happens, design features that encourage reliance, risks in edge cases, and safer ways to use them.

Korin Kashtan
The AI Industry Bubble Shapes How Technology Is Discussed Impact

The AI Industry Bubble Shapes How Technology Is Discussed

How the AI industry bubble changes tech talk into speculation—winner narratives, hype vocabulary, and shortcuts—plus a checklist to judge real performance and costs.

Paula Miller
Advanced AI Models Are Changing Expectations for Machine Reasoning Impact

Advanced AI Models Are Changing Expectations for Machine Reasoning

Advanced AI models are changing expectations for machine reasoning at work, explaining capabilities, costs, failure modes, and patterns to ship safely.

Elva Flynn
New AI Models Are Raising Expectations for Model Performance Impact

New AI Models Are Raising Expectations for Model Performance

Learn why new AI models raise the performance bar, which use cases warrant upgrades, and how to evaluate accuracy, reliability, style, and costs on real workloads.

Maurice Oliver